Analysis Of micro-Doppler Signatures Research Articles

SISO Radar-Based Human Movement Direction Determination Using Micro-Doppler Signatures

Human movement direction determination (HMDD) is a significant task in human detection and recognition applications, but it remains a challenge when utilizing single-input and single-output (SISO) radar because angle information cannot be accessed without multiple receiving antennas. Moreover, adopting multiple-output radar systems for this task would limit their applicability in a broader range of scenarios, as these systems require a larger placement area and a more extensive calibration procedure than SISO radar. Tackling this problem, this paper presents an effective method for the SISO-radar-based human movement direction determination task. Our method combines the joint time-frequency analysis (JTFA) technique with a proposed bio-inspired feature extraction process, thereby producing an accurate perception of moving direction based on the analysis of micro-Doppler signatures. The radar simulation and measurements are separately conducted and used to establish the corresponding dataset, so the superior performance of our method could be verified on the HMDD task. Furthermore, this article delves into why existing criteria for evaluating an HMDD model’s performance are insufficient in multi-direction situations, followed by an introduction of “small error concentration" and “omnidirectional error uniformity", as well as their evaluation protocols, to describe and measure the bias of an HMDD model’s results on multi-direction determination problems. By comparing with existing both traditional and deep-learning methods, we confirm our method’s superior in the HMDD task, and we believe that our research will aid in the advancement of human detection and recognition applications using SISO radar.

Detection and Localization of Unmanned Aircraft Systems Using Millimeter-Wave Automotive Radar Sensors

This letter reports the measurements and results of tests conducted on the detection of unmanned aircraft systems (UASs) or drones using a millimeter-wave (mmWave) automotive radar sensor. Designed for the detection of automotive targets with radar cross section (RCS) in excess of 20 dBsm, these miniaturized radar sensors have demonstrated detection and ranging performance in detecting a UAS, whose RCS is typically less than -15 dBsm. The sensor operates in the 76-81 GHz (mmWave) band and employs the frequency-modulated continuous-wave principle with a maximum output power of 16 mW. The detection capability of the sensor in terms of range and radial velocity measurements was investigated using a Type-I micro-UAS with a mean RCS less than $-$20 dBsm. In addition to detection, ranging, and velocity measurements, the RCS and the micro-Doppler signature measurements of the UAS in flight are also presented. The measurements and analysis suggest that these mmWave sensors are ideally suited as low-cost sensors for the detection of micro- and mini-classes of UASs at short ranges. The micro-Doppler signature analysis reveals the versatility of mmWave sensors in the extraction of micro-Doppler signatures, which serves as an effective tool for the classification of UASs from other airborne nuisance targets such as birds and moving clutter.

Micro-Doppler signature based target recognition using covariance based S-transform

Micro-Doppler signature analysis play an important role for target recognition such as fixed-target, vibrating target, moving human, vehicular motion, etc. Therefore, various signal processing based methods are reported for target recognition. Recently, Short time Fourier transform (STFT), has been applied for target recognition. In this work, we have applied Covariance based S-transform for analysis of fixed target, vibrating target. Also, the performance of Covariance based S-transform method has been compared with STFT.

Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs.

Polarimetric micro-doppler signature measurement of a small drone and its resonance phenomena

This study presents the micro-Doppler signature (MDS) of small drones based on resonance phenomena. The MDS analysis of a drone is conducted for various detection conditions. The Doppler frequencies due to the rotation of rotor blades, characterized by different materials and dimensions, are extracted at specific angular frequencies. It is confirmed that the resonance phenomenon caused by the copper-coated conducting rotor blade significantly affects the Doppler frequency. As the rotor blade dimension increases, the resonance effect moves to a lower frequency band. It is interesting to note that the resonance effect of horizontal to horizontal (HH) polarization occurs in a lower frequency band compared to vertical to vertical (VV) polarization. Additionally, it is performed in a monostatic and bistatic radar environment to analyze the resonance effect. The theoretical analysis based on full-wave simulation is confirmed via measurements with frequency-modulation continuous wave (FMCW) radar.

Loudspeaker Analysis: A Radar Based Approach

Recently, radar based micro-Doppler signature analysis has been successfully applied in various sectors including defence, biomedical, and automotive. This article presents the novel use of radar micro-Doppler for loudspeaker analysis. The approach offers the potential benefits of characterizing the mechanical motion of a loudspeaker in order to identify defects and design issues. Compared with acoustic based approaches, the use of radar allows reliable measurements in an acoustically noisy end of a production line. In addition, when compared with a laser vibrometric approach, the use of radar micro-Doppler reduces the number of measurements required and provides direct access to the information of the metallic components of the loudspeaker. In the paper experimental results and analysis of the micro-Doppler signatures of loudspeakers using low cost radar systems are presented. Based on Thiele&Small parameters, the voice coil displacement is modelled and micro-Doppler signatures for a single tone and a sine sweep stimulus are presented. Furthermore, in order to characterize the speaker with a single radar measurement, a methodology to measure mechanical frequency response of loudspeakers is also shown.

Augmented EMD for complex‐valued univariate signals

In this study, the authors propose an efficient extension of the standard empirical mode decomposition (EMD) for complex-valued univariate signal decomposition. The key idea of the extension is to convert a complex-valued univariate signal into a longer real-valued signal by augmenting the real part with the flipped imaginary part, and then to decompose it into intrinsic mode functions (IMFs) using the EMD once only. The bivariate IMFs are then retrieved from the obtained IMFs. Their empirical results on synthetic data show that the proposed method significantly outperforms the traditional bivariate EMD (BEMD) method in terms of computational efficiency while producing a comparable extraction error. Moreover, the proposed method shows better micro-Doppler signature analysis performance on physically measured continuous-wave radar data than that of the BEMD.

A UAV classification system based on FMCW radar micro-Doppler signature analysis

Due to its small size, slow flying speed, and low flying altitude, classification of mini-sized unmanned aerial vehicles (UAVs) using a frequency-modulated continuous wave (FMCW) surveillance radar is a challenging task. This is because the FMCW radar echo signals are acquired at a short dwell time and thus contain limited information about targets. In this paper, we first analyze FMCW radar returns from various types of UAVs and non-UAV objects in terms of the micro-Doppler signature (m-DS) pattern. Based on the analysis results, we propose an effective and efficient UAV classification system using FMCW radar echo signals. The proposed system consists of five main parts namely, (i) burst selection, (ii) rule-based scan pruning, (iii) the empirical mode decomposition based m-DS analysis and features extraction, (iv) error counting minimization based class label estimation, and (v) scan-to-scan filtering. Our experimental results on physically measured FMCW radar echo signals from several types of UAVs and non-UAV objects show that the proposed system consistently outperforms a commercial-off-the-shelf UAV classification system in terms of the classification accuracy.

Automatic Data-Driven Frequency-Warped Cepstral Feature Design for Micro-Doppler Classification

Micro-Doppler signature analysis and speech processing share a common approach as both rely on the extraction of features from the signal's time-frequency distribution for classification. As a result, features, such as the mel-frequency cepstrum coefficients (MFCCs), which have shown success in speech processing, have been proposed for use in micro-Doppler classification. MFCCs were originally designed to take into account the auditory properties of the human ear by filtering the signal using a filter bank spaced according to the mel-frequency scale. However, the physics underlying radar micro-Doppler is unrelated to that of human hearing or speech. This work shows that the mel-scale filter bank results in the loss of frequency components significant to the classification of radar micro-Doppler. A novel method for frequency-warped cepstral feature design is proposed as a means for optimizing the efficacy of features in a data-driven fashion specifically for micro-Doppler analysis. It is shown that the performance of the proposed frequency warped cepstral coefficients outperforms MFCC based on both simulated and measured data sets for four-class and eight-class human activity classification problems.

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

Micro‐Doppler signatures of helicopters in multistatic passive radars

This study presents an analysis of micro-Doppler signatures of helicopters obtained using a network of passive radar receivers utilising multistatic geometry. In the multistatic scenario, for each passive radar receiver the target is visible from different bistatic angles, which results in different micro-Doppler signatures. This feature allows additional information on the observed helicopter target to be obtained. The presented concept has been successfully verified by the authors using the real micro-Doppler signatures of a helicopter collected during the measurement campaign. As an illuminator of opportunity for the passive radar network a commercial digital video broadcasting-terrestrial (DVB-T) transmitter was used. The results of the applied micro-Doppler analysis for the real multistatic passive radar measurement carried out are presented in this study. In addition, some examples of inverse synthetic aperture radar (ISAR) images of helicopter rotor blades based on micro-Doppler analysis in the multistatic configuration are shown in this study.

Analysis of micro-Doppler signatures of vibration targets using EMD and SPWVD

In the micro-Doppler signature analysis of vibration targets, the joint time–frequency methods can provide useful information for target detection, classification, and recognition. In this paper, we proposed a procedure which combines both Empirical Mode Decomposition (EMD) and Smoothed Pseudo Wigner-Ville Distribution (SPWVD) techniques for the micro-Doppler analysis. The Ensemble Empirical Mode Decomposition (EEMD) based method was also proposed to address the problem of mode mixing which may occur in EMD procedure. The simulation results showed that, compared with the traditional Cohen׳s class time–frequency method (SPWVD), the method we proposed achieved much better performance under three different conditions (severe noise, weak modulation and compound vibrations).

GNSS-Based Passive Bistatic Radar for Micro-Doppler Analysis of Helicopter Rotor Blades

The alternative use of the Global Navigation Satellite System (GNSS) has recently initiated a number of studies that aim to exploit this system as an illuminator of opportunity for a passive radar system. A passive bistatic radar (PBR) configuration using a GNSS as illuminator in near forward scattering zone for micro-Doppler analysis is proposed. It is known that the received signal power is the main issue for this kind of passive radar. It is demonstrated that the enhancement achievable in received signal power strength when operating in a forward scattering mode can cope with this issue. The analysis focuses on the case of helicopters rotor blades where the Doppler shift is very high and a relatively large wavelength is useful in reducing the maximum Doppler shift. The power budget analysis for this kind of configuration and target is presented. This work demonstrates the possibility of detecting these kinds of targets and to measure their micro-Doppler signatures. The theoretical analysis is supported with simulations that demonstrate the effectiveness of the proposed configuration for micro-Doppler signature analysis for helicopter rotor blades.

Radar Micro-Doppler Signature Analysis with HHT

Radar micro-Doppler signature analysis of vibrating targets with the joint time-frequency methods can provide useful information for target detection, classification, and recognition. We employ a well-known method for nonlinear and nonstationary signals, Hilbert-Huang transform (HHT) as a new approach in micro-Doppler analysis. Simulation and experimental results show that it can achieve better performance than the traditional Cohen's class time-frequency methods.

Analysis of micro-Doppler signatures

Mechanical vibration or rotation of a target or structures on the target may induce additional frequency modulations on the returned radar signal which generate sidebands about the target's Doppler frequency, called the micro-Doppler effect. Micro-Doppler signatures enable some properties of the target to be determined. In the paper, the micro-Doppler effect in radar is introduced and the mathematics of micro-Doppler signatures is developed. Computer simulations are conducted and micro-Doppler features in the joint time - frequency domain are exploited.